Use the links below to download the installation program for Animal Space Use software available in beta test versions.
This software will allow you to apply information theoretic
methods to the analysis of space use: home range analysis and resource
selection. You'll have to use the link below which passes the file as a .zip file
which must be passed through a decompression program (e.g. Winzip) to decompress it
first into the .exe file and then run the .exe file to actually install
the program on your computer. If you are using a computer
running Windows 7 or Vista you will need to download 3 .ocx files in a zipped
archive with short instructions on placing them in the correct directories and
registering them. Earlier versions of Windows included these .ocx files
but they are no longer included and will cause various operations in Animal
Space Use to hang up without them.

Abstract: Choosing
an appropriate home range model is important for describing spaceuse
by animals and understanding the ecological processes affecting animal movement.Traditional approaches for
choosing among home range models have not resulted in general,consistent,
and unambiguous criteria that can be applied to individual data sets. We present
anew application
of information-theoretic model selection that overcomes many of thelimitations
of traditional approaches, as follows. (1) It alleviates the need to know the
truehome range to
assess home range models, thus allowing performance to be evaluated with dataon individual animals. (2)
The best model can be chosen from a set of candidate models withthe
proper balance between fit and complexity. (3) If candidate home range models
are basedon
underlying ecological processes, researchers can use the selected model not only
to describethe
home range, but also to infer the importance of various ecological processes
affectinganimal
movements within the home range.

Abstract: Fixed kernel density
analysis with least squares cross-validation (LSCVh) choice of the smoothing
parameter is currently recommended forhome-range
estimation. However, LSCVh has several drawbacks, including high variability, a
tendency to undersmooth data, and multiple localminima
in the LSCVh function. An alternative to LSCVh is likelihood cross-validation (CVh).
We used computer simulations to compareestimated
home ranges using fixed kernel density with CVh and LSCVh to true underlying
distributions. Likelihood cross-validation generallyperformed
better than LSCVh, producing estimates with better fit and less variability, and
it was especially beneficial at sample sizes <50.Because
CVh is based on minimizing the Kullback-Leibler distance and LSCVh the
integrated squared error, for each of these measures ofdiscrepancy,
we discussed their foundation and general use, statistical properties as they
relate to home-range analysis, and the biological orpractical
interpretation of these statistical properties. We found 2 important problems
related to computation of kernel home-range estimates,including
multiple minima in the LSCVh and CVh functions and discrepancies among estimates
from current home-range software. Choosing anappropriate
smoothing parameter is critical when using kernel methods to estimate animal
home ranges, and our study provides usefulguidelines
when making this decision.

Abstract: Home-range
models implicitly assume equal observation rates across the study area. Because
this assumption is frequentlyviolated,
we describe methods for correcting home-range models for observation bias. We
suggest corrections for 3 general types of home-rangemodels
including those for which parameters are estimated using least-squares theory,
models utilizing maximum likelihood for parameterestimation,
and models based on kernel smoothing techniques. When applied to mule deer (Odocoileus
hemionus) location data, we found thatuncorrected
estimates of the utilization distribution were biased low by as much as 18.4%
and biased high by 19.2% when compared tocorrected
estimates. Because the magnitude of bias is related to several factors, future
research should determine the relative influence of each ofthese
factors on home-range bias.

Abstract: By studying animal movements, researchers can gain insight into many
of theecological characteristics
and processes important for understanding population-leveldynamics.
We developed a Brownian bridge movement model (BBMM) for estimating theexpected
movement path of an animal, using discrete location data obtained at relatively
shorttime intervals. The BBMM is
based on the properties of a conditional random walk betweensuccessive
pairs of locations, dependent on the time between locations, the distance
betweenlocations, and the Brownian
motion variance that is related to the animalís mobility. Wedescribe
two critical developments that enable widespread use of the BBMM, including aderivation of the model when location data
are measured with error and a maximumlikelihood
approach for estimating the Brownian motion variance. After the BBMM is fittedto location data, an estimate of the
animalís probability of occurrence can be generated for anarea
during the time of observation. To illustrate potential applications, we provide
threeexamples: estimating animal
home ranges, estimating animal migration routes, and evaluatingthe influence of
fine-scale resource selection on animal movement patterns.

Abstract: We
propose a simple multivariate model for describing and understanding animal
space use thatestimates an
animal's probability of occurrence as an explicit function of the animal's
associationwith a fixed spatial
area (i.e., home range), the spatial distribution of resources within that area,and the occurrence of other animals. We
begin with a null model of space use to describe ananimalís
utilization distribution in the absence of effects from environmental
covariates. Wethen use this null
model as the foundation for a set of candidate models of space use thatincorporate
different combinations of environmental covariates where each model is chosen toreflect various hypotheses about important
drivers of space use. Models are parameterized viamaximum
likelihood using location data collected from individuals at discrete times
(e.g.,telemetry) and spatially
explicit environmental covariates. Information theoretic 24 criteria are usedto select the model(s) with most support
from the data. The best model(s) is then used for bothestimating
the animalís home range and for inferring the relative importance of variousenvironmental factors on space use. As an
example, we applied our approach using male whiterhino
(Ceratotherium simum)
location data collected in Matobo National Park, Zimbabwe. Thebest
synoptic model was able to capture the complexities of the utilization
distribution while themodel
structure and parameter estimates provided a basis to infer the importance of
variousecological factors
affecting male rhino space use.

Synoptic Modeling Software and detailed examples from IWMC workshop
at Durban, South Africa on 9 July, 2012 are available through this hotlink.